1057 matches found
Malicious code in vault-strategies (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 6b7037d9efc65a0885cc000a92c46ea9bed2097d02c8fb2883ceaa3eb2fd5eeb On npm install, the package's preinstall hook preinstall: node postinstall.js || true executes postinstall.js, which enumerates process.env and filte...
MAL-2026-5783 Malicious code in vault-strategies (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 6b7037d9efc65a0885cc000a92c46ea9bed2097d02c8fb2883ceaa3eb2fd5eeb On npm install, the package's preinstall hook preinstall: node postinstall.js || true executes postinstall.js, which enumerates process.env and filte...
wannacry-soc-lab
WannaCry SOC Investigation Lab Overview This project simu...
When Discovery Outpaces Remediation: Modeling AI-Accelerated Vulnerability Discovery in Interconnected Systems
Advanced AI systems for code analysis, binary analysis, fuzzing orchestration, and penetration-test planningmay significantly increase the rate at which latent vulnerabilities are discovered. While improved discovery can benefit defenders, it can also overload remediation pipelines and accelerate...
Evaluating and Combating the Impact of Concept Drift on the Performance of Machine Learning-Based Phishing Detection Systems
The expansion of the digital domain has resulted in a substantial increase in digital communication, with email emerging as one of the most prominent channels. The proliferation of email communication is apparent in both professional and personal contexts, thereby creating numerous vulnerabilitie...
Steganography without Modification: Hidden Communication Via LLM Seeds
We demonstrate that widely deployed Large Language Model LLM inference stacks harbor a steganographic channel that requires no modification to model weights, sampling code, or output distributions. The channel exploits a structural property of deterministic decoding: pseudo-random number generato...
Unveiling Privacy Risks in Multi-Modal Large Language Models: Task-Specific Vulnerabilities and Mitigation Challenges
Privacy risks in text-only Large Language Models LLMs are well studied, particularly their tendency to memorize and leak sensitive information. However, Multi-modal Large Language Models MLLMs, which process both text and images, introduce unique privacy challenges that remain underexplored...
CVE-2025-40904
A Stored HTML Injection vulnerability was discovered in the Smart Polling functionality due to improper validation of an input parameter. An authenticated user with limited privileges can push malicious remote strategies containing HTML tags through the sync. When a victim views the affected remo...
Beyond Pass/Fail: Using Process Mining to Understand How LLMs Resist (And Fail) Red Team Attacks
Standard AI red teaming evaluations reduce adversarial campaigns to a single binary outcome, attack success rate ASR, not taking into account the sequential structure of how models resist or yield to attacks. We propose applying process mining, a discipline for discovering and analyzing process...
Credential Disclosure in (EU) Digital Identity Wallets: Privacy Risks and Practical Mitigations
The European Union will introduce the EUDI Wallet by late 2026, which allows users to hold digital credentials i.e., representations of physical official identity documents on their devices. This will allow users to securely and privately disclose identity attributes to websites. Although such a...
Steering LLM Viewpoints through Fabricated Evidence Injection
As chatbots increasingly influence daily decision-making, their potential to produce misleading responses poses substantial risks to users. This paper investigates a critical cognitive vulnerability in LLMs: their tendency to uncritically trust external context when presented with fabricated...
TeleHunt: A Framework and Tool for Efficient Cybercriminal Community Discovery on Telegram
This paper presents TeleHunt, a framework and tool for evaluating the effectiveness of different strategies to discover cybercriminal communities on Telegram. TeleHunt employs a set of reference-driven snowballing strategies, integrating message-level classification, contextual filtering, and...
Description-Code Inconsistency in Real-World MCP Servers: Measurement, Detection, and Security Implications
The Model Context Protocol MCP has emerged as a critical standard empowering Large Language Models LLMs to utilize external tools. In this ecosystem, LLMs rely on natural language descriptions provided by MCP servers to select and execute functions. This interaction implicitly assumes that tool...
Quality-Diversity Evolution for Discovering Diverse Vulnerabilities in LLM Safety
Current approaches to LLM adversarial testing suffer from coverage gaps: manual red-teaming does not scale, LLM-as-attacker methods exhibit mode collapse, and gradient-based approaches produce uninterpretable gibberish. We introduce a quality-diversity evolutionary framework that operates at the...
R+R: Reassessing Java Security API Misuse in Current LLMs: A Replication on JCA and JSSE APIs with External Security Knowledge
The misuse of Java security APIs is a serious security problem in software development. Research in 2024 has shown that this problem is widespread in LLM-generated code. However, it remains unclear whether this phenomenon persists in current models and how external security knowledge affects it...
An Empirical Evaluation of LLM-Generated Code Security across Prompting Methods
The growing use of Large Language Models LLMs for automated code generation has enhanced software development efficiency, but often at the cost of security. Generated code frequently overlooks critical concerns, leaving it vulnerable to issues such as weak encryption and improper input validation...
From Preventive to Reactive: How AI Coding Assistants Transform Developers' Security Awareness
AI coding assistants are now central to professional software development, yet their impact on how developers think about and practice security remains poorly understood. While prior work has documented vulnerability rates in AI-generated code, a more fundamental question persists: how do these...
CVE-2025-40904
A Stored HTML Injection vulnerability was discovered in the Smart Polling functionality due to improper validation of an input parameter. An authenticated user with limited privileges can push malicious remote strategies containing HTML tags through the sync. When a victim views the affected remo...
CVE-2025-40904 HTML injection in Smart Polling in Guardian/CMC before 26.1.0
A Stored HTML Injection vulnerability was discovered in the Smart Polling functionality due to improper validation of an input parameter. An authenticated user with limited privileges can push malicious remote strategies containing HTML tags through the sync. When a victim views the affected remo...
CVE-2025-40904
A Stored HTML Injection vulnerability was discovered in the Smart Polling functionality due to improper validation of an input parameter. An authenticated user with limited privileges can push malicious remote strategies containing HTML tags through the sync. When a victim views the affected remo...